Two-stage battery recharge scheduling and vehicle-charger assignment policy for dynamic electric dial-a-ride services

TL;DR

This paper presents a two-stage scheduling approach for electric vehicle charging and vehicle-charger assignment in dynamic dial-a-ride services, reducing costs and waiting times through optimized planning and real-time assignment.

Contribution

It introduces a novel two-stage method combining daily charging schedules with online vehicle-charger assignment, optimized via a Lagrangian relaxation algorithm, for the first time in this context.

Findings

Achieves up to 74.9% reduction in charging waiting times.

Reduces charging times by 38.6%.

Lowers energy costs by 27.4%.

Abstract

Coordinating the charging scheduling of electric vehicles for dynamic dial-a-ride services is challenging considering charging queuing delays and stochastic customer demand. We propose a new two-stage solution approach to handle dynamic vehicle charging scheduling to minimize the costs of daily charging operations of the fleet. The approach comprises two components: daily vehicle charging scheduling and online vehicle-charger assignment. A new battery charge scheduling model is proposed to obtain the vehicle charging schedules by minimizing the costs of vehicle daily charging operations while satisfying vehicle driving needs to serve customers. In the second stage, an online vehicle-charger assignment model is developed to minimize the total vehicle idle time for charges by considering queuing delays at the level of chargers. An efficient Lagrangian relaxation algorithm is proposed to solve the large-scale vehicle-charger assignment problem with small optimality gaps. The approach is applied to a realistic dynamic dial-a-ride service case study in Luxembourg and compared with the nearest charging station charging policy and first-come-first-served minimum charging delay policy under different charging infrastructure scenarios. Our computational results show that the approach can achieve significant savings for the operator in terms of charging waiting times (-74.9%), charging times (-38.6%), and charged energy costs (-27.4%). A sensitivity analysis is conducted to evaluate the impact of the different model parameters, showing the scalability and robustness of the approach in a stochastic environment.